Chest X-ray screening, for example, is an important procedure for the detection and monitoring of lung abnormalities and diseases at an early stage in the lungs of patients. Medical personnel, such as doctors or radiologists, may detect abnormalities visually directly from the chest X-ray images. US 2009/0196481 describes a method for processing each of chest X-ray images photographed by an X-ray imaging apparatus by analyzing characteristics of lung images in the chest X-ray images, sorting the chest X-ray images, based on a result of the analysis, and displaying the result of the sorting. “Computer-Aided Diagnosis in Chest Radiographs”, National Sun Yat-Sen University, discusses a method of evaluating chest X-ray images using lung symmetry. When making a judgement about the chest radiograph, an opinion about the lung condition may be formed based on the experience of the medical personnel. For example, pulmonary oedema is an example of a disease that commonly affects the appearance of the lungs in chest X-rays and which must be monitored visually by medical personnel. Pulmonary oedema is fluid accumulation in the air spaces of the lungs. Another example is pneumonia, an inflammatory condition of the lung. In both cases, a chest X-ray image is taken, for example before symptoms are seen in order to exclude that the patients are sick. The X-ay images may then be scrutinized by a medical professional, and identification of the disease is reliant on the skill of that professional. However, interpreting information provided by the image is a challenging task and not all information is directly visible.
An article by Armato et al. “Chest Radiographs: Evaluation of Potential Utility”, Journal of Digital Imaging, Vol. 12 No. 1, February 1999, pp. 34-42, describes a method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method is used to identify the aereated lung regions in chest radiographs. The relative areas of segmented right and left lung regions in each image are compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry.
U.S. Pat. No. 4,538,227 describes a method for extracting volume information for a part of a living body from an X-ray projection image. The boundary and centroid of the part are obtained from the optimal ternary data using the variance of the gray-level of the image and the separation degree of a histogram. The volume, as well as a three-dimensional view of the part, is obtained from this data using a grey-level method.